Software development is rapidly transitioning away from traditional coding toward natural language. In this guide, we’ll go through some of the best tools for no-code and low-code web scraping.
Introduction
Data is the fuel behind the AI revolution. Whether you’re training or creating a Retrieval-Augmented Generation (RAG) pipeline, you need data. In training, your AI model ingests data to permanently learn its relationships. In a RAG-based system, your AI model uses external data to enhance its responses.
No matter your use case, you need some sort of data pipeline. Traditional pipelines require not only coding expertise but also solid grasp of Data Structures and Algorithms (DSA). With newer generations of tools, the barrier to entry is far lower.
Why data accessibility matters in AI
Every AI model is only as good as the data behind it. For many teams, the biggest challenge isn’t necessarily training the model, but data collection and preparation. Especially in fast moving industries, historical datasets simply won’t cut it — models need up to date insights for optimal performance.
As use cases of data have evolved, so have our methods and tools. Let’s take a look at the evolution of data collection through the ages.
- Antiquity: Data was collected manually and carved into stone. Data collection required a scribe.
- Middle Ages: Data collection was often the job of religious institutions. Data was used by royal and priestly classes.
- Renaissance: With increasing literacy rates, data collection was still mainly a tool of the governing class, but we begin to see things like market research and innovation happen as laypeople amass data.
- Industrial Revolution: Data collection became standardized. Scribes are no longer required for collection, people and surveys become the tools of the trade. Data is now being fitted for machines.
- Internet Age: As the world’s data migrated to the web, data collection once again became more complex. To properly handle data collection, a person needed to understand software development and web infrastructure.
- AI Revolution: As AI and no-code tools become more accessible, software development as a whole shifts away from code and toward natural language. This paradigm shift brings data collection and usage back into the hands of laypeople.
We’re entering a new age. Data collection and data driven decisions are once again accessible to laypeople without intermediaries like scribes or development teams.
No-code tools and the future of web scraping
Web scraping was once the domain of programmers. Even just a year ago, standard scraping practice required finding selectors, writing scripts, managing proxies, parsing HTML and occasionally browser automation. For most people, this was out of reach. This created a lucrative niche for extraction programmers who know how to navigate the complexities.
The data extraction landscape has changed. Today’s no-code platforms give everyone the ability to extract structured data from the web. Axiom, Browse AI, Firecrawl, Reworkd, Octoparse and Parsehub all allow you to extract structured data with zero coding required.
- Browse AI: Excellent for beginners. Point, click and fill forms — just like standard web browsing. This is perfect for recurring jobs like monitoring prices or job listings.
- Firecrawl Extract: Firecrawl does offer some code based tools but their real strength lies in their Extract tool. With Extract you can pull almost any data structure from any website.
- ParseHub: This is another point and click tool. Point, click and fill forms — dynamic content is properly handled.
- Reworkd: Reworkd allows you to launch end to end extraction pipelines with zero code or maintenance required.
- Axiom: Automate browser actions zero code. Using a simple Chrome extension, you’re able to automate full workflows.
- Octoparse: Octoparse allows you to create web based workflows using a GUI based editor — zero coding required.
No-code tools allow you to extract structured data from most sites with almost zero coding. This is where the industry is headed.
Low-code solutions for power users
No-code tools are great for average users. However, sometimes you need more granular control over your scraping tools. Sometimes you need to enter information into a site. Maybe you need proxy rotation at scale. Maybe you need specific interactions on dynamic pages with heavy JavaScript rendering.
These low-code platforms allow you to balance between simplicity and control. If you’ve even installed Linux as a daily driver, you know what we’re talking about here. Apify, Bright Data, Zyte and LangChain allow you to build incredibly complex scrapers with minimal code. Each of these providers give you powerful Integrated Development Environments (IDEs) for building your solutions.
- Apify’s IDE: Use your own logic or community built scrapers for a jumpstart that help with extraction and only a minor amount of coding.
- Bright Data Serverless Functions (IDE): Combine visual task builders with advanced logic. Prebuilt scrapers, proxy rotation, CAPTCHA solving and a multitude of export formats will help bring your data pipeline where it needs to be.
- Zyte IDE: Write, debug and deploy browser scripts all from your web browser. Integrate Zyte’s proxies with ease while writing powerful scrapers to extract your data.
- LangChain: Tools like LangChain are a fascinating piece of this puzzle and they likely support most of these other tools under the hood. With LangChain, you can write any function and wrap it as a
tool. Their tool class allows models to call any function. If you can code a function, your AI can take it from here and choose when to call that function.
Preparing AI-ready data without a full data team
Scraping your data is only the first step. Raw data is rarely usable out of the box. Whether you’re scraping or powering a RAG pipeline, the quality and structure of your data directly influence your end result. Follow our raw data rabbit hole in the next few sections. Even fictional datasets hold relationships you might not see at first glance.
Give structure to the data
Before we actually clean the data, we need to give it real structure. Take a look at the HTML snippet below. It contains a structured object, but the object is meant to render an HTML element — the snippet below is meant specifically for browsers, not AI models or their human overseers.
<h2>Alice in Wonderland</h2>
<ul>
<li>Name: Alice Liddell</li>
<li>Age: 24 years (288 months)</li>
</ul>
Now, let’s put Alice’s data into a table instead. It’s still not perfect, but we’re on our way.
| Name | First Name | Last Name | Age |
|---|---|---|---|
| Alice | Alice | Liddell | 288 months |
Cleaning and normalizing your data
Begin by removing duplicates, empty rows and irrelevant fields. Set clear standards for dates, numbers and other values. Imagine a spreadsheet.
Raw data
Take a look at the table below. It’s a mess. That’s the point. Whether human or AI, it would be very difficult to make sense of it.
| Name | First Name | Last Name | Age |
|---|---|---|---|
| Alice | Alice | Liddell | 288 months |
| Bob C. | Robert | Crane | 30 |
| Charlie | Charles | Xavier | 100 yrs |
Clean data
Now, we can take this same data and clean it up. We’ll remove duplicates and standardize all the columns.
| First Name | Last Name | Age (Years) |
|---|---|---|
| Alice | Liddell | 24 |
| Robert | Crane | 30 |
| Charles | Xavier | 100 |
Enriching the data
We can make our data even cleaner and easier to understand. Take a look at the table below. A single column adds real insight into each row of the spreadsheet. Now, we don’t just see fictional people, but where they’re from. If we were simply training on the data, this would be pretty close to done.
| First Name | Last Name | Age (Years) | Origin |
|---|---|---|---|
| Alice | Liddell | 24 | Fairy Tale |
| Robert | Crane | 30 | B (for Bob) |
| Charles | Xavier | 100 | X-Men |
Prep for AI ingestion
If our data is used after pretraining like in a RAG system, we’ve got one final thing to do. Our finished model shouldn’t be searching CSV or spreadsheets directly, this would be very inefficient. It needs an optimized database that it can search quickly.
This is where vector databases come into play. Machines really like numbers and they really like vectors. This adds an optimized way for the model to reference the data — it’s not unlike how Structured Query Language (SQL) databases hold up the backend of traditional applications and websites.
The snippet below is hardly readable for a human, but tools like LangChain and LlamaIndex can be used to convert your tables, JSON objects and spreadsheets into a format optimized for AI performance.
{
"id": "1",
"values": [0.021, -0.035, 0.918, ...], // hundreds of floats
"metadata": {
"first_name": "Alice",
"last_name": "Liddell",
"age": 24,
"origin": "Fairy Tale"
}
}
Conclusion
Today, you don’t need to be a full stack developer to collect data. You don’t need to be a developer at all! No-code and low-code tools are already here. They’re already reshaping the future of our data.
Regardless of your tools and strategic implementation, you need to think about two main things when extracting data for AI: Data structure and context. Your tools need to provide you with clean, structured data. Even raw data holds the context, but adding one simple column can make that context stick — and reduce training costs to a fraction of what they’d be using raw data.
Your data tools provide structure and reveal context. Buried inside some nasty HTML file could be anything. Without proper data tools, your AI model might never follow the rabbit hole deep enough to find Wonderland.